Transactional Conversation-Based Computing System

ABSTRACT

A conversation-based computing system may include a back-end computing module, a design module, and an execution module. The design module may be configured to provide a graphical user interface through which different conversation models are defined in metadata. Each model may include a topic containing respective goals, where the goals are associated with respective conversation flows that define respective dialogs that directs conversations toward the associated goals. Each model may also define references to topic-specific content stored in the back-end module. The execution module may be configured to execute a particular model between the system and a front-end computing device and set up integration of a live agent into the model. Execution of the model may involve, in part, carrying out, in an at least partially-automated fashion, the flow for the model according to the dialog, the topic-specific content corresponding to the model, and communicating using a specific communication protocol.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/717,787, filed Sep. 27, 2017, which is herebyincorporated by reference in its entirety. U.S. patent application Ser.No. 15/717,787 claims priority to U.S. Provisional Application No.62/510,149, filed on May 23, 2017, which is also hereby incorporated byreference in its entirety.

BACKGROUND

Enterprise software systems may be used to implement operationalprocesses for an enterprise, and may typically take the form of softwareapplications. Such applications may be developed for usage by internalusers (e.g., employees of the enterprise), as well as for external userssuch as customers that engage in transactions with the enterprise.Typically, enterprise systems use a desktop or web-based applicationhaving a forms-based user interface. In such an interface, a user mightbe presented with an electronic form in which the user fills out severalfields or entries with data. However, with the advent of mobiletechnology, a form-based application can be cumbersome to use on mobiledevices, and may sometimes request a user to provide irrelevantinformation. Further, electronic forms may be insufficient in collectingall the user's information, and thus the enterprise might not be able tomeet the user's needs. Additionally or alternatively, in some scenarios,enterprise systems may use a voice call-based interface that enables auser to place a voice call to remote agents located in call centers orelsewhere. Again, with the advent of mobile technology, such acall-based interface may be time consuming and expensive to implement.

SUMMARY

The embodiments herein improve upon the enterprise systems discussedabove and provide systems and corresponding methods for facilitatingconversation-based interaction between users and an enterprise. Such aconversation-based approach may help the enterprise quickly andefficiently ascertain a user's objective and resolve the user's issue,perhaps without the user needing to navigate a complex website, completea form, and/or make a call to a remote agent.

The conversation-based system may include a design module through whichvarious conversation models may be defined. A conversation model mayrepresent a transactional conversation between the enterprise'sconversation-based system and a user's computing device, where thetransaction conversation is tailored to meet the user's needs, theenterprise's needs, and/or the needs of another entity associated withthe enterprise (e.g., if the enterprise's system is facilitating theother entity's processes). As such, a conversation model may have atopic, various respective goals (also referred to herein as “tasks”)associated with that topic, and various conversation flows that directthe conversations towards those goals. Further, the conversation-basedsystem may include a back-end computing module in which topic-specificcontent is stored and to which the conversation model may refer.

The transactional conversations that occur between the user and theenterprise may be partially automated or fully automated, and the systemmay be arranged to switch between partial and full automation. Partialautomation may integrate into the conversation flow a remote live agentthat may assist a user in reaching the user's goals. On the other hand,full automation may occur without human input on the enterprise side.

The conversation-based system may also include an execution module thatfacilitates execution of conversation models. To execute a givenconversation model, the execution module may receive a subject from theuser's device, match the subject to a conversation model topic, andexecute a conversation flow that is determined by the conversation modeltopic. The execution of this flow may result in the implementation of aconversation between the system and the user.

The conversation-based system also includes various different protocoladapters that each uniquely connect with a user's device and support oneor more messaging systems on the user's device. The protocol adaptersmay be used to carry out conversation flows with the user's device inaccordance with a specific communication protocol that provides anadaptation of the conversation to different messaging systems. Further,to execute a given conversation module, the execution module may receivea subject from the user's device, match the subject to a conversationmodel topic, determine which communication protocol should be used forcarrying out the conversation flows of the conversation model, and thencarry out the conversation flows with the user's device.

Accordingly, a first example embodiment may take the form of a computingsystem for at least partially-automating transactional conversationsbetween the computing system and at least one front-end computingdevice. The computing system may include a back-end computing modulehaving stored topic-specific content. The computing system may alsoinclude a conversation design module configured to provide a graphicaluser interface through which a plurality of different conversationmodels are defined in metadata, where each conversation model includes atopic, the topics containing respective goals, and where the goals areassociated with respective conversation flows that define respectivedialogs that direct conversations toward the associated goals, and whereeach conversation model also defines references to the topic-specificcontent.

The computing system may also include a conversation execution modulethat includes (i) a plurality of protocol adapters, each configured for(a) connecting the conversation execution module with respectivefront-end computing devices based on characteristics of the respectivefront-end computing devices, and (b) carrying out respectiveconversation flows with the front-end computing devices according tospecific communication protocols for each of the front-end computingdevices, (ii) an application programming interface connector configuredto connect the conversation execution module with the correspondingtopic-specific content, and (iii) a conversation manager configured to(a) execute a particular conversation model between the computing systemand a particular front-end computing device of the at least onefront-end computing device, and (b) set up integration of a remote liveagent into the particular conversation model.

Execution of the particular conversation model between the computingsystem and the particular front-end computing device may involvereceiving, from the particular front-end computing device, a subject.Execution of the particular conversation model may also involve matchingthe subject to the topic of the particular conversation model. Executionof the particular conversation model may also involve determining aparticular specific communication protocol from the specificcommunication protocols based on characteristics of the particularfront-end computing device. And execution of the particular conversationmodel may also involve carrying out, in an at least partially-automatedfashion, the conversation flow for the particular conversation modelaccording to the dialog and the topic-specific content corresponding tothe particular conversation model, where carrying out the conversationflow involves communicating with the particular front-end computingdevice according to the particular specific communication protocol.

Further, a second example embodiment may take the form of a computingsystem. The computing system may include a conversation design moduleconfigured to provide a graphical user interface through which aconversation model is defined in metadata, where the conversation modelincludes a topic, the topic containing a goal, and where the goal isassociated with a conversation flow that defines a dialog that directsconversations toward the associated goal, and where the conversationmodel also defines references to topic-specific content stored in aback-end computing module. The computing system may also include aconversation execution module configured to execute the conversationmodel between the computing system and a front-end computing device.Execution of the conversation model may involve receiving, from thefront-end computing device, a subject. Execution of the conversationmodel may also involve matching the subject to the topic. Execution ofthe conversation model may also involve determining a specificcommunication protocol based on characteristics of the front-endcomputing device. And execution of the conversation model may alsoinvolve carrying out, in an at least partially-automated fashion, theconversation flow according to the based dialog and the topic-specificcontent, where carrying out the conversation flow involves communicatingwith the front-end computing device according to the specificcommunication protocol.

A third example embodiment may involve a computing system receiving, viaa graphical user interface through which a conversation model is definedin metadata, inputs defining (i) a topic of the conversation model, (ii)a goal associated with the topic, and (iii) one or more conversationprompts that make up a conversation flow of the conversation model,where the conversation flow defines a dialog that directs conversationstoward the associated goal, and where the conversation model definesreferences to topic-specific content stored in a back-end computingmodule of the computing system. The third example embodiment may alsoinvolve the computing system generating the conversation model based onthe received inputs. The third example embodiment may also involve thecomputing system executing the conversation model between the computingsystem and a front-end computing device. Execution of the conversationmodel may involve receiving, from the front-end computing device, asubject. Execution of the conversation model may also involve matchingthe subject to the topic. Execution of the conversation model may alsoinvolve determining a specific communication protocol based oncharacteristics of the front-end computing device. And execution of theconversation model may also involve carrying out, in an at leastpartially-automated fashion, the conversation flow according to thedialog and the topic-specific content, where carrying out theconversation flow involves communicating with the front-end computingdevice according to the specific communication protocol.

In a fourth example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the first,second, and/or third example embodiment.

In a fifth example embodiment, a computing system may include at leastone processor, as well as memory and program instructions. The programinstructions may be stored in the memory, and upon execution by the atleast one processor, cause the computing system to perform operations inaccordance with the first, second, and/or third example embodiment.

In a sixth example embodiment, a system may include various means forcarrying out each of the operations of the first, second, and/or thirdexample embodiment.

These as well as other embodiments, aspects, advantages, andalternatives will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a detailed system architecture diagram of aconversation-based system, as well as front-end computing devices withwhich the conversation-based systems may communicate, in accordance withexample embodiments.

FIG. 2 illustrates a simplified system architecture diagram of theconversation-based system and a representative front-end computingdevice with which the conversation-based systems may communicate, inaccordance with example embodiments.

FIG. 3 illustrates an example block diagram of a computing device, inaccordance with example embodiments.

FIG. 4A illustrates an example GUI of a conversation design module, inaccordance with example embodiments.

FIG. 4B illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 4C illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 4D illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 4E illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 4F illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 4G illustrates an example GUI of the conversation design module, inaccordance with example embodiments.

FIG. 5A illustrates an example GUI of a front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 5B illustrates an example GUI of the front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 5C illustrates an example GUI of the front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 5D illustrates an example GUI of the front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 5E illustrates an example GUI of the front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 5F illustrates an example GUI of the front-end computing system forinteracting with the conversation-based system, in accordance withexample embodiments.

FIG. 6 is a ladder diagram, in accordance with example embodiments.

FIG. 7 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

Introduction

In line with the discussion above, a conversation-based system (alsoreferred to herein as “the system”) may be used to design and thenimplement a transactional conversation model (hereinafter “conversationmodel” or “model”). The conversation model may define a dialogue of oneor more user interactions with the enterprise, and may provide aconversational interface that incorporates a conversation or series ofconversations between the user and the enterprise to perform a requestedservice that is supported by the enterprise. For example, an airline mayuse the conversation-based system to implement a conversation model ormodels that enable users to request services such as purchasing airlinetickets, filing a claim for lost luggage, changing an existingreservation, etc. A service can take various other forms as well, suchas a request for troubleshooting information, a request to accessconfidential information, and a request to track an order for a product,among other possibilities. The dialogue of the conversation model may betransactional—meaning that it may involve one or more transactionsbetween the user and the conversation-based system. Further, thedialogue may dynamically change based on user-responses to tailor theconversation to fit the user's request.

In some embodiments, an enterprise may operate the conversation-basedsystem (e.g., design and roll out various conversation models) toprovide the enterprise's services to users. However, in otherembodiments, the enterprise may operate the conversation-based system onbehalf of a client or other entity associate of the enterprise toprovide that client or associate's services to users. Further, in otherembodiments, the enterprise may operate the conversation-based system toprovide to users both the enterprise's services and client/associate'sservices, either separately, or as an integration of both services. Theclient/associate themselves may also operate the conversation-basedsystem, in some instances. In any event, for brevity's sake, the term“enterprise” may refer herein to an enterprise and/or a client of theenterprise. Furthermore, any individual (e.g., of the enterprise and/orclient/associate of the enterprise) that operates the conversation-basedsystem or any component thereof may be referred to herein as an“operator.” For example, an operator may include a designer of aconversation model, a back-end database manager, or a tester of aconversation model. In this context, a remote live agent could beconsidered an operator, but will be referred to herein as a separateentity.

In some embodiments, the conversation model may be designed using aconversation model tool. The conversation model tool may enable anoperator to define various aspects of conversations with users, such asa topic of conversation, various user-side and/or enterprise-sideactions, and the conversation flow. The topic, for instance, may referto the subject and definition of a conversation, such as what service isbeing requested and performed. For example, in the context of anairline, services such as reserving a flight, checking a flight status,tracking bags, etc., can be defined as topics. Further, for each topic,the conversation model tool may enable the operator to define the nameof the topic, goals associated with the topic, the type of each goal(e.g., user to system, or system to user), and the mode of each goal(e.g., user to automated system and no live agent, or user to automatedsystem and live agent).

The conversation model may include “snippets,” each snippet representingan interaction between the conversation-based system and the user thatoccurs in the process of achieving a given goal or goals. In someembodiments, a snippet may take the form of a prompt that theconversation-based system provides to the user. In these embodiments,each prompt may depend on the response received from the user inresponse to another prompt. For example, a given prompt may be providedbased on a single response received from the user in response to theprompt directly preceding the given prompt, or may be based on aconsideration of a series of responses to multiple preceding sequentialor non-sequential prompts. The conversation model tool may enable anoperator to define each snippet of a conversation model.

In some embodiments, the conversation model may be tailored to one ormore of the enterprise's operational processes. For example, theconversation model may understand specific language used by theenterprise (e.g., acronyms and terms) and language used by customers andemployees. In particular, the conversation model may incorporate variouslinguistic elements such as keywords, phrases, and natural languageprocessing to understand the user's input to the interaction. In somecases, the conversation model may be designed to understand user intentand behavior to better predict the user's objective and achieve theuser's goal. Further, the conversation model may learn and adapt tovarious user requests over time by tracking the progress of userinteractions, user behavior, and outcomes to better predict a user'sobjective and to more efficiently achieve the user's goal(s).

In some embodiments, the conversation model may be fully automated. In afully-automated conversational model, one or more machines of theconversation-based system may intelligently engage in a transactionalconversation with the user, and process all data and services involvedin the conversation, without input from a live human on the enterpriseside (e.g., an enterprise employee or third party). In otherembodiments, the conversation model may be partially automated, in whichthe conversation may, in a multimodal fashion, seamlessly transitionbetween automation software and user interaction with input a livehuman. For instance, depending on the user's responses to theconversation model, the conversation model may invoke the assistance ofa remote live human agent (e.g., a customer service representative ofthe enterprise) to complete the goal or service.

Herein, a “live” agent may refer to a human agent, operating remotelyfrom the user and the system, that provides real-time or non-real-timeinput to be integrated into the conversation. For example, such liveagent support may involve an agent engaging in a live audio call withthe user, or may involve the agent “taking over” the conversation andmanually typing or dictating (e.g., using Speech-to-Text) conversationsnippets that will appear as part of the conversation on the user'sfront-end computing device. Other examples are possible as well.Further, it should be noted that, in some scenarios, a “remote” agentmight not be located remotely from the system, and may use a componentof the system to provide assistance.

In some embodiments, the conversation-based system may be implemented ona user's device (e.g., a smart phone, laptop computer, desktop computer,tablet, smart glasses, an augmented reality device, a virtual realitydevice, etc.) and may be “portable”—namely, the system may be configuredto tie the conversation model to multiple different front-end computingsystems (e.g., different user devices). Phrased another way, theconversation model may be logical and might not depend on the physicaldevice on which it executes. By having separation between the logicalconversational model and its physical realization, the conversationmodel may achieve portability across many different front-end computingsystems by providing a logical-to-physical mapping layer of software.Accordingly, the conversation-based system may be implemented onmultiple different front-end computing systems having differentoperating environments. Further, the conversation based-system mayoperate on different platforms such as the enterprise's nativeapplication, a text messaging application, or a social media messagingapplication that executes on the front-end computing system.

The disclosed conversation-based system may include various graphicaluser interfaces (GUIs). For example, the conversation-based system mayimplement a GUI as part of a conversation model tool operating on aconversation design module of the system. Through this tool, variousconversation models may be created for an enterprise, or perhaps forclients of the enterprise. As another example, the conversation-basedsystem may implement on front-end computing systems various GUIs throughwhich the user can experience various conversation models. Such GUIs maybe provided on the enterprise's native application or on otherapplications that execute on front-end computing systems. In someembodiments, GUIs on different front-end computing systems may appearsimilar, so that the enterprise can maintain a similar style, look, andfeel to the GUI used by its customers and employees. In otherembodiments, the style, look, and feel to the GUI may vary fromapplication to application, or from front-end system to front-endsystem.

The following embodiments describe architectural and functional aspectsof example conversation-based computing systems, as well as the featuresand advantages thereof.

II. Example Computing Devices and Conversation-Based ComputingEnvironments

FIG. 1 illustrates a detailed system architecture diagram of an exampleconversation-based system, as well as front-end computing devices withwhich the conversation-based system may communicate, in accordance withexample embodiments described herein. As shown, the exampleconversation-based system includes a conversation design module 100(hereinafter “design module”), a conversation execution module 102(hereinafter “execution module”), and a back-end computing module 104.Each of these modules may be wirelessly connected and/or connected via awired connection (e.g., Ethernet).

The design module 100 may include a GUI 106, a conversation model 108,and a systems catalog 110. The design module 100 may provide GUI 106 viaa display device of the design module 100, such as a touch screen orcomputer monitor, and the operator may interact with GUI 106 via aninput device such as a touch screen, keyboard, trackpad, and/or mouse.In some embodiments, GUI 106 may be provided as part of a conversationmodel tool that facilitate the creation and management of metadata inwhich conversation models are defined. Phrased another way, the operatormay use GUI 106 and the conversation model tool to define some or allaspects of a conversation model described herein, such as topics, goals,etc.

Conversation model 108 is a representative example of a conversationmodel that an operator may design using the design module 100. In someembodiments, conversation model 108 may be stored either in local memoryof the design module 100 (not shown) or in remote memory at anotherdevice, such as execution module 102 or back-end computing module 104.As shown, conversation model 108 includes a topic 112, goals 114,actions 116, data 118, snippets 120, flow 122, language 124, and meaning126, each of which may be represented as metadata. In other embodiments,conversation model 108 may include other elements in addition to oralternatively to those shown in FIG. 1.

A topic such as topic 112 may refer to the subject and definition of aconversation, such as what service is being requested and performed, asnoted above. A topic may represent one instantiation of the conversationmodel, and may be an umbrella under which various user interactions andconversations fall.

In some embodiments, each topic may contain respective goals, and thustopic 112 may contain goals 114. For example, a topic such as “Return MyOrder” may contain various goals, such as creating a ticket with theenterprise, scheduling a return shipment, and then ultimately tracking arefund. In turn, each goal may have a definition, a type, a mode, andinstances (of both data and actions), among other possible elements. Insome embodiments, the definition of the goal may be a high-leveldefinition of the goal (e.g., “Return My Order”).

Further, the type of the goal may specify whether the goal involves theconversation-based system receiving information from the user (e.g.,“Consumer to System”) and/or the user receiving information from theconversation-based system (e.g., “System to Consumer”). The mode of thegoal may specify whether the goal, in whole or in part, supports userinteraction with the system only in a fully-automated mode (i.e., nolive agent integration), or supports live agent integration in apartially-automated mode. Further, for a goal involving the userreceiving information from the conversation-based system, variousnotifications may appear on the user's device, such as notifications toresume the conversation.

The execution and completion of a goal may include any action instancesor data instances that occur during completion of the goal. The actionsof a conversation model (e.g., actions 116) may be any actions that theuser and/or system performs as part of the conversation model. Suchactions may be bound to object metadata 128 and/or action metadata 130stored in systems catalog 110.

Objects (also referred to herein as “data objects”) may be instances ofdata entities that participate in a given conversation. For example, ina conversation regarding flight information and/or luggage, data objects may include one or more specific flight reservations that areapplicable to the user and/or luggage tag IDs of one or more bags thatthe user has checked for their flight. As another example, in aconversation regarding the return of ordered items, data objects mayinclude the items of the order that are being returned. Object metadata128 may thus refer to a metadata definition of the data entities. Forexample, object metadata for an account object may refer to thedefinition of the account data entity. Each account (e.g., Person X′saccount, Account #123) may thus be an instance of that account dataentity. Further, action metadata 130 may refer to a metadata definitionof actions that may be performed during a given conversation (e.g.,scheduling a delivery, booking a flight, returning an item). Forexample, action metadata for a shipment action may refer to thedefinition of the shipment service.

Moreover, actions may be represented as tasks, services, events, or thegoals themselves. Tasks may be actions that are performed internallywithin the conversation-based system (e.g., creating a ticket). Further,services may be a more general form of an action that is invoked on anexternal system. Still further, events may be actions that areasynchronously triggered and that may result in notifications to users(e.g., a notification to a user that an order is ready for pickup).

By way of example, actions for the goal of “Return My Order” may includea user creating a ticket, the system notifying the user that the tickethas been created, the user requesting a status and tracking update forthe return, and the system providing a particular status and trackingupdate for the return. Other examples are possible as well. Furthermore,each action may have a type and a mode, such as the type and modedescribed above.

Data 118, as shown in FIG. 1, refers to data that may be used toconfigure or manage the actions 116 and instances that occur as part ofconversation model 108. In some embodiments, such data may be data thatthe design module receives from back-end computing module 104 or anyother enterprise system. For instance, data 118 may define references totopic-specific content 132 or user profile data 134 that is stored atback-end computing module 104. Topic-specific content relevant toconversation model 108 may then be integrated into conversation model108 during execution based on those defined references.

Topic-specific content 132 may include enterprise content relating to avariety of conversation topics, and thus each conversation model mayincorporate at least a portion of this content. For example, thetopic-specific content for an airline company may include airlinebaggage allowance policies, airline flight times and schedules, delayinformation, customer service, etc., some or all of which may berelevant to a variety of topics, such as booking flights, changingflights, cancelling flights, finding lost luggage, and determiningbaggage policies, among other possibilities.

User profile data 134 may include data relating to a profile that theuser has established with the enterprise. For example, user profile datafor an airline company may include a user's account with the airlinecompany, including accumulated miles, previous flights, the user'sfrequent flying number, etc. Further, user profile data 134 may includedata related to the user's device (e.g., a front-end computing device).In particular, user profile data 134 may include device data and devicecharacteristics/capabilities that the user has given permission for theenterprise to access, such as the device's mobile equipment identifier(MEID), device type (e.g., smartphone), a history of global positioningsystem (GPS) locations of the device, an identification of applicationsinstalled on the device, device settings, time entries for previousconversations, time entries for other user device actions, and/or devicecapabilities (e.g., camera, microphone, or other resources). Otherexamples are possible as well.

Examples of data 118 may include data objects and fields related totopic 112, and context data. As noted above, data objects may beinstances of data entities that participate in a given conversation. Insome embodiments, each topic 112 may include one or more fields, each ofwhich may define information that is to be obtained from the user and/orprovided by the system during the conversation. For instance, forreturning an order, one field may be an item selection field where theuser will select which product item to return. A field may have a type,such as a predefined list of items from which the user may select.

Further, context data may include, for example, a user/device GPSlocation, a user device type, a time or times a user has performedvarious actions, and/or a user profile with the enterprise, among otherpossibilities. In some embodiments, at least some of such context datamay be the same as user profile data 134, and may be stored at thedesign module or imported from back-end computing module 104.

Snippets 120, in line with the discussion above, may representinteractions that are part of conversation model 108 and that occurbetween the conversation-based system and the user in the process ofachieving a given goal or goals. One or more snippets may be associatedwith a potential user response to a preceding snippet. In someembodiments, a snippet may take the form of a prompt that theconversation-based system provides to the user, such as “Can you pleaseselect which item you want to return?” As described above, each promptmay depend on the response received from the user in response to anotherprompt. However, in some embodiments, each prompt might not depend onthe response received from the user in response to another prompt, butmay rather be selected in some other manner, such as in one of themanners discussed below.

Flow 122 represents a conversation flow for conversation model 108. Insome embodiments, a conversation flow may define respective dialogs thatdirect conversations toward their associated goals (e.g., goals 114).Further, in some embodiments, flow 122 may support one or more types ofdialogs, including, but not limited to: (i) rules-based dialogs, (ii)graph-based dialogs, and/or (iii) artificial intelligence driven(AI-driven) dialogs. In some embodiments herein, the term “rules-based”may refer to any dialog that is performed in accordance with a set ofrules and may thus encompass both graph-based dialogs and AI-drivendialogs.

At a high level, rules-based dialogs may involve performing a particulardialog action in response to detecting a particular input. By way ofexample, the conversation model may be configured to look for aparticular user input (e.g., a response to a prompt), and, in responseto detecting the user input, the conversation model may responsivelyprovide the user with a particular prompt or other information. Asanother example, the conversation model may be configured to look forparticular terms or a particular type of sentence structure in theuser's input, and, in response to detecting such terms or structure, theconversation model may responsively provide the user with a particularprompt or other information.

Graph-based dialogs may involve various paths (e.g., branches of atree-based structure) that the conversation may follow to achieve adesired result (e.g., completion of a goal). In some embodiments, theconversation-based system may implement techniques such as heuristics todetermine which path to follow in the conversation.

AI-driven dialogs may incorporate various AI-based technology andtechniques, such as machine learning. In some embodiments, theconversation-based system may use machine learning to generate and/orcarry out at least a portion of conversation model 108.

In some embodiments, rules-based, graph-based, and/or AI-drivenconversation models may be configured to implement a variety of languageprocessing elements and AI-related techniques to better adapt to users,maintain a history of user interactions and corresponding outcomes,learn user intent, behavior, and patterns, predict user actions, andthus improve conversation flow over time and more efficiently interactwith users to accomplish goals. For example, the conversation model mayimplement language processing elements such as natural languageprocessing, text mining, keyword recognition/analysis, phraserecognition/analysis, voice (e.g., the user's voice)recognition/analysis, sentence structure recognition/analysis,introspective data model management and analytics, and/or predictiveanalytics, among other elements. Collectively, such elements are shownin FIG. 1 as “Language 124.” Furthermore, as another example, theconversation model may implement AI-related techniques such as sentimentanalysis, semantics analysis, and/or intent analysis, among otherpossibilities. Collectively, such elements are shown in FIG. 1 as“Meaning 126.” Other examples are possible as well.

Further, the conversation-based system may use any of the techniquesdescribed above to ascertain an understanding and meaning of specificlanguage used by the enterprise (e.g., acronyms and terms associatedwith the enterprise) and by the user (e.g., shorthand) in conversations.

Referring back to FIG. 1, the execution module 102 may include fourprotocol adapters 132, 134, 136, and 138 (each abbreviated as “PA,” asshown). In other embodiments, execution module 102 may include adifferent amount of protocol adapters. In addition, execution module 102may include a conversation manager 140, a logical conversation protocol(LCP) 142 that interfaces between the protocol adapters to theconversation manager 140, and an application programming interface (API)connector 144.

Further, also shown in FIG. 1 are four representative front-endcomputing devices 146, 148, 150, and 152. In line with the discussionabove, each front-end computing device may be a smart phone, laptopcomputer, desktop computer, tablet, smart glasses, or other type ofdevice, that is configured to engage in wireless or wired communicationwith the conversation-based system over a network such as the Internet.

In some embodiments, each of the protocol adapters may be configured toconnect execution module 102 with each of the front-end computingdevices based on respective characteristics of the front-end computingdevices. Such characteristics may include, by way of example, anoperating environment of the front-end device, an operating system ofthe front-end device, one or more user interfaces of the front-enddevice, and/or one or more applications installed on the front-enddevice. Regarding applications, for instance, execution module 102 mayinclude a protocol adapter configured for communication with front-enddevices via a native application on the front-end device (e.g., nativeApple® operating system application built with Swift®, or nativeAndroid™/Windows® Phone application built with C#), a web application(e.g., an application built in JavaScript, HTML5, and/or Cascading StyleSheets (CSS), and accessible via the web), and/or a hybrid application(i.e., an application installed similar to a native application, butthat functions as a web application).

To facilitate connection of execution module 102 with each of thefront-end computing devices, each protocol adapter may be uniquelyconfigured to support communication (e.g., execution of the conversationmodel) with a front-end device having particular characteristics. Inparticular, upon execution of the conversation model, execution module102 may determine certain characteristics of the front-end device withwhich the conversation will occur (e.g., which native or non-nativemessaging systems/applications the front-end device supports), and mayset up a particular corresponding protocol adapter to carry out theconversation flow with the front-end device in accordance with aspecific communication protocol supported by the protocol adapter andthe front-end device. For example, such a specific communicationprotocol may be an application layer protocol such as Short MessageService (SMS), Facebook® Messenger, and Extensible Messaging andPresence Protocol (XMPP). In essence, each protocol adapter may beconfigured to adapt the conversation to various front-end devices. Forinstance, the conversation-based system may include a Facebook®Messenger protocol adapter for connecting to a Facebook® Messengerapplication and adapt the conversation flow to that application.

In a particular example, referring to FIG. 1, front-end device 146 mayhave installed the enterprise's native application, and PA 132 may beconfigured to carry out conversation flows on the enterprise's nativeapplication. Further, front-end device 148 may have installed a mobileweb application (e.g., an application on an airline company's website),and PA 134 may be configured to carry out conversation flows on the webapplication. Still further, front-end device 150 may have installed asocial media messaging application (e.g., Facebook® Messenger orSlack®), and PA 136 may be configured to carry out conversation flows onthe social media messaging application. And still further, front-enddevice 152 may have installed a SMS application, and PA 138 may beconfigured to carry out conversation flows on the SMS application. Otherexamples are possible as well.

Furthermore, LCP 142 may take the form of a generic messaging protocolaccording to which conversation model(s) 154 could be carried out bydefault. In some embodiments, LCP 142 may be configured to convert(e.g., translate) more specific protocols, such as those discussed abovewith regard to the protocol adapters, to and from the generic messagingprotocol.

In some embodiments, as shown, conversation manager 140 may have adirect or indirect connection with back-end computing module 104, and inturn, a connection with topic-specific content 132 and user profile data134. As such, this arrangement may enable conversation manager 140 toleverage predefined triggers and actions in a corresponding conversationmodel and integrate the particular topic-specific content into theconversation model using the references defined in the conversationmodel.

In some embodiments, API connector 144 may include one API connector ormultiple API connectors, each of which may be configured to connectexecution module 102 with one or more external systems 155 so that APIconnector 144 may then access data from, and/or perform actions on,external system(s) 155. For example, in a scenario where theconversation flow involves retrieving flight information for a user'sflight, execution module 102 may use API connector 144 to request andthen receive the flight information from an external system associatedwith a travel technology company and/or airline. As another example, ina scenario where the conversation flow involves a request for weatherinformation, execution module 102 may use API connector 144 to requestand then receive the weather information from an external systemassociated with a weather company. Other examples are possible as well.Furthermore, in some embodiments, an API connector that is associatedwith particular topic-specific content may enable conversation manager140 to leverage predefined triggers and actions in a correspondingconversation model and integrate the particular topic-relevant externalcontent into the conversation model using the references defined in theconversation model.

In some embodiments, execution of a particular conversation modelbetween the communication-based system and a front-end device mayinvolve execution module 102 receiving a subject from the front-enddevice, which execution module 102 may then match to a correspondingtopic, and thus to a corresponding conversation model. The subject maytake the form of data representative of a user input related to thetopic. For instance, if the user selects a “Return or Replace Items”command of an application running on the front-end device, the front-enddevice may transmit data representative of that command, and executionmodule 102 may then determine that the command corresponds to the topicof “Return or Replace Items” and thus select the conversation modelcorresponding to that topic. In particular, conversation manager 140 maysearch through conversation model(s) 154 for the conversation modelcorresponding to that topic.

Furthermore, execution of a particular conversation model may involveexecution module 102 using characteristics of the front-end device as abasis for determining a particular specific communication protocol foruse in communicating (e.g., carrying out the model's conversation flow)with the front-end device. For instance, execution module 102 maydetermine what kind of messaging protocols the front-end devicesupports, such as SMS or a social media messaging protocol, and selectone of those protocols for use as the specific communication protocolaccording to which the system may communicate with the front-end device.To facilitate this, for example, execution module 102 may refer to astored user-profile associated with the front-end device to determinewhich protocols the front-end device supports. Other examples arepossible as well.

Execution of a particular conversation model may further involveexecution module 102 carrying out, in an at least partially-automatedfashion, the conversation flow for the particular conversation modelaccording to a particular dialog (e.g., rules-based, graph-based, and/orAI-driven, depending on the conversation model) and also according tothe topic-specific content corresponding to the particular conversationmodel.

As discussed above, execution module 102 may execute the conversationflow of the particular conversation model in a partially-automatedfashion or a fully-automated fashion. To facilitate this, conversationmanager 140 may be further configured to set up integration of a remotelive agent 156 into the particular conversation model. Live agent 156may then provide real-time or non-real-time input to be integrated intothe conversation. For example, as noted above, live agent integrationmay involve live agent 156 engaging in a live audio call with the user,or may involve live agent 156 “taking over” the conversation andproviding conversation snippets that will appear as part of theconversation on the front-end device.

In some embodiments, the particular conversation model may be designedto incorporate optional live agent integration, where the user is givenan option at one or more times during the conversation to speak with orotherwise communicate with a live agent. As another example, theparticular conversation model may be designed to incorporate mandatorylive agent integration, where the user is required to communicate with alive agent in order to complete one or more goals. In some embodiments,the particular conversation model may define whether the live agentintegration is optional or mandatory.

In some embodiments, once integration of live agent 156 is initially setup, execution module 102 may continue to facilitate ongoing integrationof live agent 156 into the conversation. Further, to help live agent 156best understand the user's issue and avoid providing duplicate promptsto which the user has already responded, execution module 102 mayprovide live agent 156 with a transcript or other data that indicateswhat has been discussed as part of the conversation thus far, includinguser responses and prompts that have already occurred. For instance,execution module 102 may transmit this data to a computing device oflive agent 156. Additionally or alternatively, execution module 102 mayprovide a GUI through which live agent 156 may view the data. Otherexamples are possible as well.

The blocks represented in FIG. 1 are for purpose of example. Theconversation-based system may include more or fewer components, and theoperations of each component may vary.

FIG. 2 illustrates a simplified system architecture diagram of theconversation-based system 200 and a representative front-end computingdevice 202 (user device) with which the conversation-based system maycommunicate, in accordance with example embodiments described herein. Asnoted above, conversation-based system 200 may include a conversationdesign module 204, a back-end computing module 206, and a conversationexecution module 208.

Conversation design module 204 may include a GUI 210 similar to the GUIdiscussed above, as well as metadata 212 in which one or moreconversation models are defined.

Back-end computing system 206 may include topic-specific content 214,such as content specific to various services provided by the enterprise,to which various conversation models may define references.

Conversation execution module 208 may include conversation protocoladapters 216, a conversation manager 218, and a connector 220, each ofwhich may be configured similarly to the protocol adapters, conversationmanager, and API connector, described above with regard to FIG. 1. Forinstance, conversation execution module 208 may execute a conversationmodel between the conversation-based system 200 and the front-endcomputing device 202 over a network such as Internet 222 using protocoladapters 216, in accordance with a particular communication protocol,and on a variety of platforms on the front-end computing device 202.

As shown, Internet 222 may represent a portion of the global Internet.However, Internet 222 may alternatively represent a different type ofnetwork, such as a private wide-area or local-area packet-switchednetwork.

In addition, connector 220 may have a connection with external system(s)224. In line with the discussion with respect to FIG. 1, connector 220may be configured to use this connection to access data from, and/orperform actions on, external system(s) 224.

FIG. 3 illustrates an example block diagram of a computing device 300,illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 300 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. In particular, computing device 300 performoperations of a component of the conversation-based system describedherein, such as the back-end computing module, the design module, and/orthe execution module.

In this example, computing device 300 includes processor(s) 302(referred to as “processor 302” for sake of simplicity), networkinterface(s) 304, an input/output unit 306, main memory 308, and a massstorage device 310, all of which may be coupled by a system bus 312 or asimilar mechanism. In some embodiments, computing device 300 may includeother components and/or peripheral devices (e.g., detachable storage,printers, and so on).

Processor 302 may be any type of computer processing unit, such as acentral processing unit (CPU), a co-processor (e.g., a mathematics,graphics, or encryption co-processor), a digital signal processor (DSP),a network processor, and/or a form of integrated circuit or controllerthat performs processor operations. In some cases, processor 302 may bea single-core processor, and in other cases, processor 302 may be amulti-core processor with multiple independent processing units.Processor 302 may also include register memory for temporarily storinginstructions being executed and related data, as well as cache memoryfor temporarily storing recently-used instructions and data.

Network interface(s) 304 may take the form of a wireline interface, suchas Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Networkinterface(s) 304 may also support communication over non-Ethernet media,such as coaxial cables or power lines, or over wide-area media, such asSynchronous Optical Networking (SONET) or digital subscriber line (DSL)technologies. Network interface(s) 304 may also take the form of awireless interface, such as IEEE 802.11 (Wi-fi), BLUETOOTH®, globalpositioning system (GPS), or a wide-area wireless interface. However,other forms of physical layer interfaces and other types of standard orproprietary communication protocols may be used over networkinterface(s) 304. Furthermore, network interface(s) 304 may comprisemultiple physical interfaces. For instance, some embodiments ofcomputing device 100 may include Ethernet, BLUETOOTH®, and Wi-fiinterfaces.

Input/output unit 306 may facilitate user and peripheral deviceinteraction with example computing device 300. Input/output unit 306 mayinclude one or more types of input devices, such as a keyboard, a mouse,a touch GUI, and so on. Similarly, input/output unit 306 may include oneor more types of output devices, such as a GUI, monitor, printer, and/orone or more light emitting diodes (LEDs). Additionally or alternatively,computing device 300 may communicate with other devices using auniversal serial bus (USB) or high-definition multimedia interface(HDMI) port interface, for example.

Main memory 308 may be any form of computer-usable memory, including butnot limited to register memory and cache memory (which may beincorporated into processor 302), as well as random access memory (RAM)314 and read-only memory (ROM) 316. Other types of memory may includebiological memory. Memory such as ROM 316 may contain basic routines tohelp transfer information between elements of the computing device 300.

Main memory 308 may store program instructions and/or data on whichprogram instructions may operate. By way of example, main memory 308 maystore these program instructions on a non-transitory, computer-readablemedium, such that the instructions are executable by processor 302 tocarry out any of the methods, processes, or operations disclosed in thisspecification or the accompanying drawings.

Mass storage device 310 may take the form of or contain a file system inwhich an operating system 318 (represented in FIG. 3 as “OS”),applications 320, and associated data, are stored. The operating system318 may include modules for memory management, scheduling and managementof processes, input/output, communication, and/or otherwise controllingthe operation of the computing device 300. Applications 320 may be oneor more user-space software programs, such as web browsers or emailclients, as well as any software libraries used by these programs.Further, operating system 318 may include device drivers that enable theoperating system 318 to communicate with the hardware modules (e.g.,memory units, networking interfaces, ports, and busses), of computingdevice 300.

Although illustrated as separate components, in practice, main memory308 and mass storage device 310 may be combined into a single componentin some embodiments.

In some embodiments, the computing device 300 may operate in a networkedenvironment using the network interface(s) 304 to connect to remotenetwork devices through a network, such as wireless network 322, theInternet, or another type of network. The network interface(s) 304 maybe used to connected to other types of networks and remote computingsystems as well.

III. Example Conversation-Based GUIs

Example GUIs will now be described in the context of example servicesprovided by an example enterprise. Alternatively, in line with thediscussion above, the example services described herein may be servicesprovided by a client or other associate of an enterprise, in which casethe enterprise may use the conversation-based system to facilitateprovisioning of these services using the conversation-based system.

In particular, FIGS. 4A, 4B, 4C, 4D, 4E, 4F, and 4G, each relate toservices that could be provided by an online store or similarenterprise. Further, each of these figures provides an example GUI ofthe design module through which a conversation model can be defined.

FIG. 4A illustrates an example GUI 400 that the design module mayprovide for creating or editing topics of various conversation models.As discussed above, GUI 400 may be provided as part of a conversationmodel tool. In practice, an operator may access GUI 400 to add a newtopic, edit an existing topic, or delete an existing topic. The threeconversation model topics shown in FIG. 4A relate to an online store,and include: “Get help with my product,” “Return or Replace Items,” and“Track My Order.” Each of these three topics may have an associated GUIelement, such as elements 402, 404, and 406, respectively. In addition,GUI 400 may include a “New Topic” element 408 that the operator canselect to add a new topic. Further, each topic may include an “Actions”drop-down menu element 410 that, when selected, causes a drop-down menuto appear.

FIG. 4B illustrates an example GUI 412 including an “Actions” drop-downmenu 414 for the “Return or Replace Items” topic. In some embodiments,the design module may enable an operator to modify or create a topicusing JavaScript Object Notation (JSON), and such options may beincluded in the drop-down menu 414, among other options. For instance,as shown, the drop-down menu includes options such as “Edit,” “Clone,”“Edit With JSON Editor,” “View Raw JSON,” and “Delete.”

FIG. 4C illustrates an example GUI 416 for editing a conversation model.In particular, GUI 416 may be displayed in response to selecting theoption to edit the “Return or Replace Items” topic illustrated in FIG.4B, which may define a conversation that the conversation-based systemmay have with a user for returning or replacing a purchased item.

In this example, the operator may use GUI 416 to provide various topicinformation, such as the topic name (e.g., the “Alias” element 418) andthe topic description (e.g., the “Conversation Summary” element 420).Further, in the “Keywords” element 422, the operator may providekeywords associated with the topic, namely, keywords that indicate tothe system that the user is seeking to return or replace an item. Stillfurther, GUI 416 may include an element that can be used to select acustomer relationship management (CRM) system includes (e.g., the “CRMSystem” element 424) to use for the topic. A CRM system may be a systemthrough which the enterprise (or client of the enterprise) can compileand manage data related to its customers to better understand customerrequests and goals. Further, GUI 416 may include an element forselecting whether to enable live agent integration into the conversationmodel (e.g., the “Live Agent” element 426).

In addition, as shown, GUI 416 may provide the name of one or moreexternal systems that participate in the conversation, and may identifyvarious actions that are provided by that system (e.g., a flight companyback-end system that provides flight information, where retrieving areservation and updating a reservation might be two actions provided bythat system).

FIG. 4D illustrates an example GUI 428 for adding, editing, and/ordeleting goals (i.e., “Tasks,” as shown) of the conversationmodel—namely, for the “Track My Order” topic. As shown, GUI 428 includesthree columns relating to the goals: “Title,” “Name,” “Type,” and“Mode.” In FIG. 4D, the “Title” of the goal may represent a high-leveldefinition of the goal. Further, the “Name” of the goal identifies eachaction of the goal, and the “Type” of the goal may specify whether theaction is an action where the conversation-based system receivesinformation from the user (e.g., “Consumer to System”) or an actionwhere the user receives information from the conversation-based system(e.g., “System to Consumer”). It should be noted that GUI 428 identifieseach action as a respective goal, thereby totaling six goals. Stillfurther, the “Mode” column specifies whether each action of the goalwill support user interaction with the system only in a fully-automatedmode (i.e., no live agent integration), or will support live agentintegration in a partially-automated mode. As shown, each goal isassociated with a mode entitled “Qmode,” which may be an example type ofmode incorporating a prompt-based, question and answer interaction,where a user's answer to a given prompt may determine one or moresubsequent prompts. Other types of modes are possible as well. Forinstance, another mode may incorporate a voice-based interaction, andyet another mode may incorporate a conversation interaction templateaccording to which the conversation may occur.

By way of example, as shown in GUI 428, the goal of “Create Ticket” hastwo actions: (i) “CreateTicket,” a “Consumer to System” action where theuser creates a ticket with the system to track the user's order, and(ii) “Notification,” a “System to Consumer” action where the systemsends the user a notification related to creation of the ticket. Next,the goal of “Check Status” has two actions: (i) “CheckStatus,” a“Consumer to System” action where a user requests that the systemprovide a status of the user's order, and (ii) “Shipped,” a “System toConsumer” action, where the system informs the user that the order'sstatus is that it has been shipped. Lastly, the goal of “Track My Order”has two actions as well: (i) “TrackOrder,” a “Consumer to System” actionwhere the user requests the system to provide tracking information forthe user's order, and (ii) “ConfirmDelivery,” a “System to Consumer”action where the system sends the user a tracking update that the orderhas been delivered. Other examples are possible as well.

Each goal may include additional or alternative actions in practice. Forinstance, “Check Status” may include an action where the order's statusis that the order is still being processed and has not yet shipped(e.g., “In Processing,” rather than “Shipped”), and “Track My Order” mayinclude an action where the system sends the user a tracking update thatthe order is en route, perhaps specifying an exact location.

FIG. 4E illustrates an example GUI 430 for editing or adding one or moresnippets associated with a goal. As shown, GUI 430 includes fiveprompts, each of which ask the user to provide information regarding theitem to be returned, such as “Why do you want to return this item?” and“Can you take a picture of the “+Vars.vItemName+”?”

GUI 430 may enable the operator to provide a variety of metadatarelating to each prompt. For example, the prompt for “Here are your mostrecent purchases, can you pick the product in which you have aproblem”/“Can you pick the item you would like to return?” includes a“Field” element 432 that defines information to be obtained from theuser with regard to the given prompt—namely, that the user will have toidentify which product they have a problem with or would like to return(i.e., “Selectedltem,” as shown). Further, this prompt includes a“Required” element 434, with which the operator can specify whether theuser will be required to identify the product, and a “Type” element 436,with which the operator can specify the manner in which the user will berequired to identify the product (e.g., by taking a “Picture” of theproduct, as shown). Still further, this prompt includes a “PromptMessage” element 438 where the operator can type what the prompt willask. Other GUI elements are possible as well.

FIG. 4F illustrates an example GUI 440 for editing or adding a fieldassociated with a topic. In particular, GUI 440 illustrates a “Reason”field that relates to a user-selection of a reason for why the userseeks to return or replace an ordered product. The field is named in the“Name” element 442 of the GUI 440. Further, GUI 440 includes a “Type”element 444 that indicates that the “Reason” field is a “Static Picker”type—namely, a predefined list of items from which the user may select.As such, GUI 440 also includes a “Pick Map” section 446 with three“Value” elements 448, 450, and 452, where the operator can define whatthe “Reason” list of selectable items comprises, such as “Arriveddamaged,” “Changed my mind,” and “Doesn't fit.”

FIG. 4G illustrates an example GUI 454 for managing integration of theconversation model with the back-end computing module. In line with thediscussion above, the back-end computing module may include data relatedto a live agent chatting system (“LiveAgent,” as shown), a user-specificprofile or information relating to the user (e.g., the user's accountwith the enterprise) or the user's front-end computing system(“UserInfo,” as shown), or topic-specific content of a particular entity(“Entity1,” as shown) or entities, such as the enterprise and/or one ormore clients of the enterprise. Accordingly, at a high level, GUI 454 ora similar GUI may enable the operator to manage how the execution moduleintegrates this data with the design and execution of the conversationmodel. For instance, as shown, GUI 454 includes a “Connector Library”section 456 listing API connectors and channels associated withtopic-specific content for Entity 1, the live agent SMS chatting system,and user profile/device information. Furthermore, although not shown inGUI 454, the operator may use a GUI related to GUI 454 to import data,such as particular entity's topic-specific content, from the back-endcomputing module into the conversation model based on the references tosuch data that are defined in the conversation model.

FIGS. 5A, 5B, 5C, 5D, 5E, and 5F, illustrate various GUIs of a mobileapplication running on a user's front-end computing device. Inparticular, these figures illustrate an example scenario in which theuser converses with an airline company via a conversation modelimplemented on the airline's mobile application. One or moreconversations that these figures illustrate may relate to one or moretopics, goals, actions, fields, etc., that an operator may define usingthe design module in the manner discussed above. Further, it should benoted that although the GUIs illustrated by these figures resemble aninstant messaging or SMS application, conversations such as those shownin the figures are primarily between a user/client device and acomputing system—namely, the conversation-based system.

In line with the discussion above, an enterprise may typically providevarious mechanisms for customers or other users to engage intransactions with the enterprise. Such mechanisms may include web-basedinterfaces, telephone calls with customer service representatives,and/or instant messaging systems with a live representative, among otherpossibilities. However, there may be disadvantages to each of thesemechanisms. For example, web-based interfaces may be awkwardly arrangedand outdated, and/or may contain large electronic forms requesting userinput of irrelevant information. These forms may present difficulties tousers of smaller devices, such as smartphones and tablets. As anotherexample, a telephone call or instant messaging system may beunproductive and inefficient if representatives are required to beavailable to assist users at all times, even with simple requests, or ifrepresentatives are unavailable for certain days and times.

The present disclosure thus provides a user-friendly mechanism—namely,the conversation-based computing system—for engaging in transactionswith an enterprise. The conversation-based system may, for instance,provide a more efficient, user-friendly way to obtain information from auser and help reach a user's goal, without tying up unnecessaryresources. Further, the ability of this system to be flexibly configuredand customized to carry out particular conversations facilitates rapiddeployment of sophisticated conversational capabilities. Consequently,the embodiments herein are technical tools that enable transactions tobe performed according to a predefined set of customizable rules.

The figures described below illustrate examples of these and otherimprovements and advantages provided by the conversation-based system.

FIG. 5A illustrates an example GUI 500 that enables a user to input arequest into the application. As shown, the user types a questionregarding how many bags the user is able to check, and varioussuggestions are auto-populated as the user types. Each of thesuggestions shown—“Baggage Allowance,” “Report Delayed Bag,” and “TrackMy Bags”—may correspond to a particular topic and, in turn, to aparticular conversation model designed at the design module. Althoughnot shown, if the user then selects “Baggage Allowance,” for instance, aGUI may appear that displays information regarding the airline's baggageallowance policy, such as text for various rules, a number of allowedbags, and a corresponding size of each bag that is allowed.

FIG. 5B illustrates an example GUI 502 that may be displayed at somepoint after the user selects “Report Delayed Bag.” In particular, GUI502 comprises at least two sections: a messaging section 504 and aselection section 506. The messaging section 504 displays theconversation that occurs between the user and the system. The selectionsection 506 displays an interface including selectable options. In thisexample, the three selectable options shown each correspond to adifferent bag the user checked. Accordingly, the application enables theuser to easily select a bag tag number corresponding to the delayed bag.To facilitate this, an operator may use the design module to define theconversation in the messaging section 504 as well as the selectableoptions displayed in the selection section 506. Further, the applicationmay interface with the airline company via the conversation-based systemto import relevant user information for use in the conversation, such asthe user's flight information and bag tag information. Such informationmay be stored in the back-end computing module or elsewhere.

FIG. 5C illustrates an example GUI 508 that may be displayed after auser has uploaded an image of their bag 510 in response to beingprompted with the instruction “Please tap to provide picture of yourbag.” In practice, such an image may be helpful so that a record of thebag tag and the corresponding image of the bag can be added to a recordof the user's claim. As discussed above, each user response may affectthe subsequent prompt in a repeated process until the goal is complete,and perhaps thereafter. For instance, as shown, the conversation modelmay be defined such that, in response to the user uploading a picture ofthe user's delayed bag, conversation model may ask the user to provide abrief description of the bag.

FIG. 5D illustrates an example GUI 512 that displays a multimodalexperience. In particular, as shown, the user's description of thedelayed bag (e.g., “Black”) prompts the system to provide the user withtwo types of messages: an automated message 514 and a non-automatedmessage 516. The automated message 514 may inform the user that thesystem is attempting to locate the user's bag and also provides the userwith a file reference number. Further, a remote live airline agent mayenter the non-automated message 516 to be integrated into theconversation. In particular, the non-automated message 516 informs theuser of the agent's name (“Richard,” as shown) and confirms to the userthat the airline is searching for her bag.

FIG. 5E illustrates an example GUI 518 that displays a customer serviceoption 520 provided by the airline company via the application to chatwith an agent. In some scenarios, the conversation model may enable theuser to connect with an agent even after one or more of the user's goalshas been completed.

FIG. 5F illustrates an example GUI 522 that displays a live conversationwith an agent. In the scenario shown, the user informs the agent thatthe user changed a hotel in which the user is staying. Accordingly, theagent may intervene by updating the location at which the bag should bedelivered.

Other GUIs may be displayed in practice for a variety of topics andcorresponding to a variety of conversations. For instance, the airlineconversation model discussed above may involve asking the user toprovide a location to where the delayed bag should be forwarded anddisplay a GUI including a map for the user to use to search for andselect the address. The user may then respond by select a forwardingaddress on a map provided for the user. Further, the airlineconversation model discussed above may involve displaying a GUIincluding a selection section and one or more selectable options thateach correspond to a different time slot indicating when the bag may bedelivered. Accordingly, the user may select the time during which thebag should be delivered to the identified location. As discussed above,the conversation-based system may import information from other sourcessuch as the airline or other external sources to facilitate provision ofcertain services such as these. For instance, the selectable time slotsmay correspond to available times when the bag may be delivered, and thetime slots may be based on various factors, such as time slots after thearrival of the next flight carrying the bag.

As discussed above, conversation models may be implemented on a varietyof front-end computing devices via platforms other than a mobileapplication of the enterprise or the enterprise's client. Such otherplatforms may include a social media application. For instance, a usermay use a social media messaging application to track a productpurchased online or return or replace a product. In line with thediscussion above, the user may input a request to “Track My Order” or“Return or Replace Item,” and the system may then execute acorresponding conversation model on the user's device via the socialmedia messaging application. Other examples are possible as well.

IV. Example Third-Party Application Integration

In some embodiments, a front-end computing device may be configured toaccess the conversation-based system via a third-party applicationinstalled on the front-end computing device. In particular, such athird-party application may take the form of a messaging applicationprovided by a third-party entity with which the user has an account. Thethird-party application may take other forms as well.

A particular authentication procedure may be implemented in suchembodiments. Through this procedure, the front-end computing device, oneor more third-party computing devices, and the conversation-based systemmay interface to link the user's account for the third-party applicationwith the user's account with the conversation-based system (e.g., auser's account with the enterprise that manages the conversation-basedsystem). Once this has occurred, the conversation-based system mayimplement conversation models on the front-end computing system via thethird-party application.

Some of the computing devices described below may be referred to as“cloud-based” devices, meaning that they may be housed at various remotedata center locations. The exact physical location, connectivity, andconfiguration of these computing devices may be unknown and/orunimportant to front-end computing devices.

FIG. 6 illustrates operations of an example authentication procedure inthe form of a ladder diagram. As shown, FIG. 6 illustrates varioustransactions and messages between a third-party application 600 (e.g., athird-party application running on a user's front-end computing device),a third-party platform 602 (e.g., a third-party-managed, cloud-basedcomputing device, such as a server), an enterprise instance third-partyendpoint 604 (e.g., an enterprise-managed, cloud-based computing devicethat is registered with the third-party application and that isconfigured to interface third-party devices with the enterprise system),and an enterprise instance authentication server 606 (e.g., anenterprise-managed, cloud-based server). In practice, the enterpriseinstance authentication server 606 may be configured to manageauthentication procedures associated with third-party applications, andmay additionally be configured to manage other types of authenticationprocedures as well. In other embodiments, the example authenticationprocedure may include more or less transactions and messages than thoseshown in FIG. 6, and/or may include more or less computing entities.

In accordance with the operations of FIG. 6, the third-party platform602 may receive a chat message from the third-party application 600.This chat message may be a conversation initiation message intended forreceipt by the conversation-based system. The message may be part of aconversation model or may be a message that the user sends via thethird-party application 600 to request initiation of the execution of aconversation model on the front-end computing device. The third-partyplatform 602 may then transmit, to the enterprise instance third-partyendpoint 604, the chat message as a string along with metadata that mayinclude a verification token, a user's username, a name of thethird-party application, and/or a name of a team to which thethird-party application belongs, among other possible information.

Next, the enterprise instance third-party endpoint 604 may transmit theverification token to the enterprise instance authentication server 606so that the enterprise instance authentication server 606 can thenvalidate the verification token. The enterprise instance third-partyendpoint 604 may transmit other information to the enterprise instanceauthentication server 606 as well, such as any of the metadata listedabove, and the chat message itself. The enterprise instanceauthentication server 606 may then verify the authenticity of the chatmessage by verifying the verification token in the chat message againsta token that has been registered by the third-party entity with theenterprise for the third-party application. Then, based on the metadatareceived by way of the third-party platform 602, the enterprise instanceauthentication server 606 may make a determination of whether the user'saccount with the third-party application has already been linked to theuser's account with the enterprise.

If the determination is that the accounts have been linked, then theprocedure may terminate and the user may be enabled to interface withthe conversation-based system via the third-party application. On theother hand, if the determination is that the accounts have not beenlinked, the enterprise instance authentication server 606 may transmit alogin challenge to the third-party application 600 via the enterpriseinstance third-party endpoint 604 and the third-party platform 602. Thelogin challenge may take various forms. By way of example, the loginchallenge may take the form of a hyperlink or other type of link, andmay prompt the user to enter the user's credentials for the user'saccount with the enterprise in order to trigger the user's account withthe enterprise to be linked with the user's account with the third-partyapplication. Once the user has clicked the link and logs in using theuser's credentials for the user's enterprise account, the enterpriseinstance authentication server 606 may then link the user's enterpriseaccount to the user's third-party application account. The user may thusbe enabled to interface with the conversation-based system via thethird-party application.

Furthermore, once the accounts have been linked, the enterprise instanceauthentication server 606 may transmit a callback message to theenterprise instance third-party endpoint 604, the receipt of which maytrigger the enterprise instance third-party endpoint 604 to transmit tothe third-party application 600, via the third-party platform 602, anotification that the accounts have been linked. The front-end computingsystem may then provide the user with the notification, such as bydisplaying the notification on a GUI for the third-party application.

In some embodiments, the enterprise instance authentication server 606may append a message token to messages transmitted from an enterprisedevice to a third-party device (e.g., from the enterprise instanceauthentication server 606 to the third-party platform 602, by way of theenterprise instance third-party endpoint 604). This message token may beregistered at the time an interface between the third-party device(s)and the enterprise device(s) is set up. The third-party device(s) may beconfigured to expect such a message token with every message that itreceives from the enterprise, or else the third-party device(s) maydisregard the message. Further, these message tokens, as well as tokensfor messages that the enterprise device(s) received from the third-partydevice(s), may be cached by each such device.

The procedure described above might occur only upon a user's first timeaccessing the conversation-based system via the third-party application,and/or may occur at other times, such as after a threshold amount oftime has passed since the user last accessed the system, after thethird-party application has been uninstalled and then reinstalled on theuser's device, etc.

V. Example Operations

FIG. 7 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 7 may be carried out by a computing device, such ascomputing device 300, and/or a group of computing devices, such as themodules of the conversation-based system (e.g., conversation-basedsystem 200) described herein. However, the process can be carried out byother types of devices or device subsystems. For example, the processcould be carried out by a portable computer, such as a laptop or atablet device.

The embodiments of FIG. 7 may be simplified by the removal of any one ormore of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or as otherwise described herein.

Block 700 may involve receiving, by a computing system, via a graphicaluser interface through which a conversation model is defined inmetadata, inputs defining (i) a topic of the conversation model, (ii) agoal associated with the topic, and (iii) one or more conversationprompts that make up a conversation flow of the conversation model. Theconversation flow may define a dialog that directs conversations towardthe associated goal. And the conversation model may define references totopic-specific content stored in a back-end computing module of thecomputing system.

Block 702 may involve based on the received inputs, generating, by thecomputing system, the conversation model.

Block 704 may involve executing, by the computing system, theconversation model between the computing system and a front-endcomputing device, where execution of the conversation model involves (i)receiving, from the front-end computing device, a subject; (ii) matchingthe subject to the topic; (iii) determining a specific communicationprotocol based on characteristics of the front-end computing device; and(iv) carrying out, in an at least partially-automated fashion, theconversation flow according to the dialog and the topic-specificcontent, where carrying out the conversation flow involves communicatingwith the front-end computing device according to the specificcommunication protocol.

In some embodiments, the computing system may generate the conversationmodel further based on additional inputs as well. For example, thecomputing system may receive, via the graphical user interface,additional inputs representing a plurality of conversation snippetsassociated with the goal, and may use these received additional inputsas a further basis for generating the conversation model. Eachconversation snippet may be representative of a single prompt providedby the computing system to the front-end computing device, and at leastone conversation snippet may be associated with a potential response toa preceding conversation snippet received by the computing system fromthe front-end computing device.

Additionally or alternatively, as another example, the computing systemmay receive, via the graphical user interface, additional inputsrepresenting one or more fields, and may use these received additionalinputs as a further basis for generating the conversation model. Thetopic of the conversation model may include the one or more fields, andthe one or more fields may define information that the computing systemis to request from the front-end computing device during theconversation flow for the conversation model.

Additionally or alternatively, as another example, the computing systemmay receive, via the graphical user interface, additional inputsrepresenting one or more keywords, and may use these received additionalinputs as a further basis for generating the conversation model. Thetopic of the conversation model may include the one or more keywords,and the one or more keywords may define terms or phrases that thecomputing system predicts to be received from the front-end computingdevice during an initial stage of the conversation flow for theconversation model.

In some embodiments, carrying out the conversation flow for theconversation model in the at least partially-automated fashion mayinvolve carrying out the conversation flow for the conversation model ina fully-automated fashion, without involvement of the remote live agent.

In some embodiments, carrying out the conversation flow for theconversation model in the at least partially-automated fashion mayinvolve carrying out the conversation flow for the conversation model ina partially-automated fashion, and involving the remote live agent inthe conversation model for at least a portion of the conversation flow.

In some embodiments, the computing system may be managed by anenterprise, and the conversation model may correspond to a serviceprovided by a third-party client of the enterprise to consumers of thethird-party client.

In some embodiments, the computing system may include a back-endcomputing module that stores the topic-specific content, a conversationdesign module that is configured to perform operations such as theoperations of Blocks 700 and 702, and/or a conversation execution modulethat is configured to perform operations such as the operations of Block704.

In some embodiments, the conversation execution module may include aprotocol adapter configured for connecting the conversation executionmodule with the front-end computing device and carrying out theconversation flow with the front-end computing device according to thespecific communication protocol.

In some embodiments, the conversation execution module may include anapplication programming interface connector configured to connect theconversation execution module with the topic-specific content.

In some embodiments, the conversation execution module may be configuredto set up integration of a remote live agent into the conversationmodel.

In some embodiments, the computing system may include a back-endcomputing module having stored topic-specific content. In theseembodiments, the computing system may also include a conversation designmodule configured to provide a graphical user interface through which aplurality of different conversation models are defined in metadata. Eachconversation model may include a topic, the topics may containrespective goals, and the goals may be associated with respectiveconversation flows that define respective dialogs that directconversations toward the associated goals. Further, each conversationmodel may also define references to the topic-specific content. In theseembodiments, the computing system may also include a conversationexecution module including a plurality of protocol adapters, anapplication programming interface connector, and a conversation manager.

Each of the plurality of protocol adapters may be configured for (a)connecting the conversation execution module with respective front-endcomputing devices based on characteristics of the respective front-endcomputing devices, and (b) carrying out respective conversation flowswith the front-end computing devices according to specific communicationprotocols for each of the front-end computing devices. The applicationprogramming interface connector may be configured to connect theconversation execution module with the corresponding topic-specificcontent. The conversation manager may be configured to (a) execute aparticular conversation model between the computing system and aparticular front-end computing device of the at least one front-endcomputing device, and (b) set up integration of a remote live agent intothe particular conversation model.

In these embodiments, execution of the particular conversation modelbetween the computing system and the particular front-end computingdevice may involve receiving, from the particular front-end computingdevice, a subject. Such execution may further involve matching thesubject to the topic of the particular conversation model. Suchexecution may further involve determining a particular specificcommunication protocol from the specific communication protocols basedon characteristics of the particular front-end computing device. Andsuch execution may further involve carrying out, in an at leastpartially-automated fashion, the conversation flow for the particularconversation model according to the dialog and the topic-specificcontent corresponding to the particular conversation model. Carrying outthe conversation flow may involve communicating with the particularfront-end computing device according to the particular specificcommunication protocol.

Furthermore, in some embodiments, the conversation design module may beconfigured to receive, via the graphical user interface, inputsrepresenting the topics of the particular conversation model, the goalsfor each topic, and, for each goal, a plurality of conversationsnippets. Each conversation snippet may be representative of a singleprompt provided by the computing system to the particular front-endcomputing device, and at least one conversation snippet may beassociated with a potential response to a preceding conversation snippetreceived by the computing system from the particular front-end computingdevice. Further, the conversation design module may be configured togenerate the particular conversation model based on the received inputs.

In some embodiments, each topic may include one or more fields thatdefine information that the computing system is to request from theparticular front-end computing device during the conversation flow forthe particular conversation model. In these embodiments, theconversation design module may be configured to receive, via thegraphical user interface, additional inputs representing the one or morefields, and generate the particular conversation model further based onthe received additional inputs.

In some embodiments, each topic may include one or more keywords thatdefine terms or phrases that the computing system predicts to bereceived from the particular front-end computing device during aninitial stage of the conversation flow for the particular conversationmode. In these embodiments, the conversation design module may beconfigured to receive, via the graphical user interface, additionalinputs representing the one or more keywords, and generate theparticular conversation model further based on the received additionalinputs.

In some embodiments, carrying out the conversation flow for theparticular conversation model in the at least partially-automatedfashion may involve carrying out the conversation flow for theparticular conversation model in a fully-automated fashion, withoutinvolvement of the remote live agent.

In some embodiments, carrying out the conversation flow for theparticular conversation model in the at least partially-automatedfashion may involve carrying out the conversation flow for theparticular conversation model in a partially-automated fashion, and mayalso involve involving the remote live agent in the particularconversation model for at least a portion of the conversation flow.

In some embodiments, setting up integration of the remote live agentinto the particular conversation model may involve providing fordisplay, on one or more of a remote computing device of the remote liveagent and a display device of the computing system, the conversationflow of the particular conversation model that was carried out beforethe integration of the remote live agent into the particularconversation model.

In some embodiments, the computing system may be managed by anenterprise, and the back-end computing module may have stored aplurality of topic-specific content. Each topic-specific content maycorrespond to, and be tailored to, a distinct third-party client of theenterprise. Further, the plurality of different conversation models mayinclude, for each third-party client, at least one conversation modelthat defines references to the topic-specific content corresponding tothe third-party client.

In some embodiments, each conversation model may correspond to arespective service provided by an enterprise to consumers of theenterprise and/or a respective service provided by a third-party clientof the enterprise to consumers of the third-party client of theenterprise.

In some embodiments, each of the plurality of protocol adapters may befurther configured for carrying out conversation flows with thefront-end computing devices via a third-party application installed onthe front-end computing devices.

VI. Conclusion

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, compact-discread only memory (CD-ROM), for example. The computer readable media canalso be any other volatile or non-volatile storage systems. A computerreadable medium can be considered a computer readable storage medium,for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

1. A computing system comprising: a semi-automated conversationexecution module configured to execute a conversation model between thecomputing system and a front-end computing device, and establishintegration of a live agent into the conversation model, wherein theconversation model includes a topic, the topic being associated with oneor more goals, wherein respective goals of the one or more goals areassociated with respective conversation flows that define respectiverules-based dialogs that direct conversations toward the associatedgoals, wherein the conversation model defines one or more points duringexecution of the conversation model at which to invoke the integrationof the live agent, and wherein the integration of the live agentcomprises communication between the live agent and the front-endcomputing device to address at least one of the one or more goals; andmemory storing conversation data that represents a conversation flow ofthe conversation model between the computing system and the front-endcomputing device, wherein execution of the conversation model betweenthe computing system and the front-end computing device comprises:carrying out the conversation flow between the front-end computingdevice and the semi-automated conversation execution module to producethe conversation data; determining that the integration of the liveagent has been invoked at one of the one or more points defined by theconversation model; in response to determining that the integration ofthe live agent has been invoked at one of the one or more points definedby the conversation model, integrating the live agent into theconversation model; and providing for display, on a computing device ofthe live agent, the conversation data.
 2. The computing system of claim1, wherein the conversation model defines which of the one or more goalssupport the one or more points during execution of the conversationmodel at which to invoke the integration of the live agent.
 3. Thecomputing system of claim 1, wherein carrying out the conversation flowbetween the front-end computing device and the semi-automatedconversation execution module comprises sending, to the front-endcomputing device, automated messages as part of the conversation flow,and wherein execution of the conversation model between the computingsystem and the front-end computing device further comprisesdiscontinuing the sending of the automated messages as part of theconversation flow while the live agent is integrated into theconversation model.
 4. The computing system of claim 1, whereinintegrating the live agent into the conversation model comprisesestablishing a live video call between the computing device of the liveagent and the front-end computing device.
 5. The computing system ofclaim 1, wherein integrating the live agent into the conversation modelcomprises establishing a live audio call between the computing device ofthe live agent and the front-end computing device.
 6. The computingsystem of claim 1, wherein execution of the conversation model betweenthe computing system and the front-end computing device furthercomprises: receiving, from the computing device of the live agent,response data provided by the live agent for integration into theconversation flow; and integrating the response data into theconversation flow.
 7. The computing system of claim 6, wherein theresponse data includes one or more of: audio data, video data, or textdata.
 8. The computing system of claim 1, wherein the one or more pointsdefined by the conversation model include at least one point duringexecution of the conversation model at which to provide the front-endcomputing device with a selectable option for invoking the integrationof the live agent, and wherein determining that the integration of thelive agent has been invoked at one of the one or more points defined bythe conversation model comprises receiving input representing selectionof the selectable option for invoking the integration of the live agent.9. The computing system of claim 1, wherein the one or more pointsdefined by the conversation model include at least one point duringexecution of the conversation model at which to automatically triggerintegration of the live agent.
 10. The computing system of claim 1,further comprising: a conversation design module configured to provide agraphical user interface through which the conversation model is definedand further configured to receive, via the graphical user interface,input representing whether at least one goal of the one or more goalswill integrate the live agent into the conversation model.
 11. Thecomputing system of claim 1, wherein at least one of the one or morepoints defined by the conversation model includes a point after the oneor more goals have been addressed.
 12. A method comprising: carryingout, between a front-end computing device and a semi-automatedconversation execution module of a computing system, a conversation flowof a conversation model, wherein the conversation model includes atopic, the topic being associated with one or more goals, whereinrespective goals of the one or more goals are associated with respectiveconversation flows that define respective rules-based dialogs thatdirect conversations toward the associated goals, wherein theconversation model defines one or more points during execution of theconversation model at which to invoke integration of a live agent intothe conversation model, wherein the integration of the live agentcomprises communication between the live agent and the front-endcomputing device to address at least one of the one or more goals, andwherein carrying out the conversation flow produces conversation datathat represents the conversation flow of the conversation model betweenthe computing system and the front-end computing device; determiningthat the integration of the live agent has been invoked at one of theone or more points defined by the conversation model; in response todetermining that the integration of the live agent has been invoked atone of the one or more points defined by the conversation model,integrating the live agent into the conversation model; and providingfor display, on a computing device of the live agent, the conversationdata.
 13. The method of claim 12, wherein the conversation model defineswhich of the one or more goals support the one or more points duringexecution of the conversation model at which to invoke the integrationof the live agent.
 14. The method of claim 12, wherein carrying out theconversation flow between the front-end computing device and thesemi-automated conversation execution module comprises sending, to thefront-end computing device, automated messages as part of theconversation flow, the method further comprising: discontinuing thesending of the automated messages as part of the conversation flow whilethe live agent is integrated into the conversation model.
 15. The methodof claim 12, further comprising: receiving, from the computing device ofthe live agent, response data provided by the live agent for integrationinto the conversation flow; and integrating the response data into theconversation flow.
 16. The method of claim 12, wherein the one or morepoints defined by the conversation model include at least one pointduring execution of the conversation model at which to provide thefront-end computing device with a selectable option for invoking theintegration of the live agent, and wherein determining that theintegration of the live agent has been invoked at one of the one or morepoints defined by the conversation model comprises receiving inputrepresenting selection of the selectable option for invoking theintegration of the live agent.
 17. The method of claim 12, wherein theone or more points defined by the conversation model include at leastone point during execution of the conversation model at which toautomatically trigger integration of the live agent.
 18. An article ofmanufacture including a non-transitory computer-readable medium, havingstored thereon program instructions that, upon execution by a computingsystem, cause the computing system to perform operations comprising:carrying out, between a front-end computing device and a semi-automatedconversation execution module of the computing system, a conversationflow of a conversation model, wherein the conversation model includes atopic, the topic being associated with one or more goals, whereinrespective goals of the one or more goals are associated with respectiveconversation flows that define respective rules-based dialogs thatdirect conversations toward the associated goals, wherein theconversation model defines one or more points during execution of theconversation model at which to invoke integration of a live agent intothe conversation model, and wherein the integration of the live agentcomprises communication between the live agent and the front-endcomputing device to address at least one of the one or more goals, andwherein carrying out the conversation flow produces conversation datathat represents the conversation flow of the conversation model betweenthe computing system and the front-end computing device; determiningthat the integration of the live agent has been invoked at one of theone or more points defined by the conversation model; in response todetermining that the integration of the live agent has been invoked atone of the one or more points defined by the conversation model,integrating the live agent into the conversation model; and providingfor display, on a computing device of the live agent, the conversationdata.
 19. The article of manufacture of claim 18, wherein theconversation model defines which of the one or more goals support theone or more points during execution of the conversation model at whichto invoke the integration of the live agent.
 20. The article ofmanufacture of claim 18, wherein carrying out the conversation flowbetween the front-end computing device and the semi-automatedconversation execution module comprises sending, to the front-endcomputing device, automated messages as part of the conversation flow,the operations further comprising: discontinuing the sending of theautomated messages as part of the conversation flow while the live agentis integrated into the conversation model.